---
title: "About TimeGPT"
description: "Learn about TimeGPT - the foundation model for time series."
icon: "brain"
---

<br />

<Info>
TimeGPT is a production-ready generative pretrained transformer model specifically designed for time series forecasting. It accurately forecasts domains such as retail, electricity, finance, and IoT with minimal code. Below you'll find a high-level overview of its features, architecture, and practical examples.
</Info>

<br />

<Steps>
  <Step title="1. Start Now">
    <CardGroup>
      <Card title="Activate Free Trial" href="https://dashboard.nixtla.io/freetrial" description="Begin exploring TimeGPT right away with a free trial." />
      <Card title="Quickstart Guide" href="/forecasting/timegpt_quickstart" description="Follow step-by-step instructions to get TimeGPT running quickly." />
    </CardGroup>
  </Step>

  <Step title="2. Choose Your Interface">
    You can access TimeGPT through:

      - Self-hosted deployment on your infrastructure (recommended): [book a call](https://meetings.hubspot.com/cristian-challu/enterprise-contact-us?uuid=dc037f5a-d93b-4%5B…%5D90b-a611dd9460af&utm_source=github&utm_medium=pricing_page) for more information
      - Hosted APIs: start your [free trial](https://dashboard.nixtla.io/sign_in)
      - Azure Studio (TimeGEN-1)

  </Step>

  <Step title="3. Forecast or Detect Anomalies">
    Perform zero-shot inference out-of-the-box to forecast future values or detect anomalies. Fine-tune the model if you need more targeted performance.
  </Step>
</Steps>

<Check>
For detailed instructions and advanced configurations, visit our
[Quickstart Guide](/forecasting/timegpt_quickstart) and additional tutorials.
</Check>

## Features and Capabilities

<AccordionGroup>
  <Accordion title="Zero-shot Inference">
    **[Zero-shot Inference](/forecasting/timegpt_quickstart)**:
    Generate forecasts and detect anomalies immediately without prior training. Quickly gain insights from your data.
  </Accordion>

  <Accordion title="Fine-tuning">
    **[Fine-tuning](/forecasting/fine-tuning/steps)**:
    Enhance prediction accuracy by training TimeGPT on your own datasets, tailoring it to your unique scenario.
  </Accordion>

  <Accordion title="API Access">
    **[API Access](https://dashboard.nixtla.io/sign_in)**:
    Integrate forecasts into applications via a robust API. Easily obtain keys at the
    [Dashboard](https://dashboard.nixtla.io/sign_in).
    Easily deploy TimeGPT in your own infrastructure or with any cloud provider using [Docker](/setup/docker) or our Python [wheel file](/setup/python_wheel).
    Also accessible in [Azure Studio](/setup/azureai) or through private deployment.
  </Accordion>

  <Accordion title="Add Exogenous Variables">
    **[Add Exogenous Variables](/forecasting/exogenous-variables/numeric_features)**:
    Incorporate external variables (e.g., events, prices) to improve forecast accuracy.
  </Accordion>

  <Accordion title="Multiple Series Forecasting">
    **[Multiple Series Forecasting](/forecasting/timegpt_quickstart)**:
    Predict multiple time series at once, improving workflow efficiency.
  </Accordion>

  <Accordion title="Specific Loss Function">
    **[Specific Loss Function](/forecasting/fine-tuning/custom_loss)**:
    Customize training with loss functions that match your performance objectives.
  </Accordion>

  <Accordion title="Cross-validation">
    **[Cross-validation](/forecasting/evaluation/cross_validation)**:
    Evaluate model reliability and generalization with built-in cross-validation.
  </Accordion>

  <Accordion title="Prediction Intervals">
    **[Prediction Intervals](/forecasting/probabilistic/prediction_intervals)**:
    Generate intervals to capture forecast uncertainty.
  </Accordion>

  <Accordion title="Irregular Timestamps">
    **[Irregular Timestamps](/forecasting/special-topics/irregular_timestamps)**:
    Process data with non-uniform timestamps directly, with no extra preprocessing.
  </Accordion>

  <Accordion title="Anomaly Detection">
    **[Anomaly Detection](/anomaly_detection/real-time/introduction)**:
    Identify anomalies automatically, integrating external features for improved precision.
  </Accordion>
</AccordionGroup>

<Info>
Get started quickly with the
[Quickstart guide](/forecasting/timegpt_quickstart). Explore in-depth tutorials on TimeGPT capabilities and real-world applications.
</Info>

## Architecture

<Frame caption="TimeGPT Architecture Overview">
  ![TimeGPT Architecture Overview](https://github.com/Nixtla/nixtla/blob/main/nbs/img/timegpt_archi.png?raw=true)
</Frame>

TimeGPT's architecture builds on the self-attention mechanism introduced in the original ["Attention is All You Need"](https://arxiv.org/abs/1706.03762) paper. Unlike typical large language models (LLMs), TimeGPT is independently trained on extensive time series datasets to minimize forecasting errors.

<Info>
TimeGPT employs an encoder-decoder structure with residual connections, layer normalization, and a linear output layer to match the decoder outputs to forecast dimensions. The attention-based mechanisms help the model capture diverse historical patterns to create accurate future predictions.
</Info>

The model processes input sequences from left to right, similar to how humans read sentences, and predicts future values (*"tokens"*) based on historical windows of time series data.

## Explore Examples and Use Cases

<AccordionGroup>
  <Accordion title="Quickstart & Setup">
    Quickly set up your workflow using our
    [Quickstart Guide](/forecasting/timegpt_quickstart)
    or learn to use the API by
    [setting up your API key](/setup/setting_up_your_api_key).
  </Accordion>
  <Accordion title="Practical Topics">

      - [Anomaly Detection](/anomaly_detection/real-time/introduction)

      -
        Fine-tuning with
        [custom loss functions](/forecasting/fine-tuning/custom_loss)


      -
        Scaling workflows using
        [Spark](/forecasting/forecasting-at-scale/spark),
        [Dask](/forecasting/forecasting-at-scale/dask), or
        [Ray](/forecasting/forecasting-at-scale/ray)


      -
        Integrating
        [exogenous variables](/forecasting/exogenous-variables/numeric_features),
        validation with
        [cross-validation](/forecasting/evaluation/cross_validation),
        and estimating uncertainty via
        [quantile forecasts](/forecasting/probabilistic/quantiles)
        or
        [prediction intervals](/forecasting/probabilistic/prediction_intervals).



  </Accordion>
  <Accordion title="Real-world Applications">

      - [Web Traffic Forecasting](/use_cases/forecasting_web_traffic)

      - [Bitcoin Price Prediction](/use_cases/bitcoin_price_prediction)


  </Accordion>
</AccordionGroup>

<Check>
With TimeGPT, you can rapidly iterate from initial exploration to high-accuracy forecasting. Dive deeper into the comprehensive tutorials for more sophisticated workflows.
</Check>
